Admiraal Ryan, Handcock Mark S
Department of Statistics, University of Washington, Box 354322, Seattle WA 98195-4332, United States of America.
Department of Statistics, University of Washington, Seattle WA 98195-4332, United States of America.
J Stat Softw. 2008 Feb;24(8). doi: 10.18637/jss.v024.i08. Epub 2008 May 8.
The ability to simulate graphs with given properties is important for the analysis of social networks. Sequential importance sampling has been shown to be particularly effective in estimating the number of graphs adhering to fixed marginals and in estimating the null distribution of graph statistics. This paper describes the package for R and how its simulate and simulate_sis functions can be used to address both of these tasks as well as generate initial graphs for Markov chain Monte Carlo simulations.
模拟具有给定属性的图的能力对于社交网络分析很重要。顺序重要性抽样已被证明在估计符合固定边际的图的数量以及估计图统计量的零分布方面特别有效。本文描述了用于R的软件包,以及如何使用其simulate和simulate_sis函数来完成这两项任务,以及为马尔可夫链蒙特卡罗模拟生成初始图。